A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea

Renewable and Sustainable Energy Reviews 122 (2020) 109725

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A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea KiJeon Nam a, 1, Soonho Hwangbo b, 1, ChangKyoo Yoo a, * a

Dept. of Environmental Science and Engineering, College of Engineering, Center for Environmental Studies, Kyung Hee University, Seocheon-dong 1, Giheung-gu, YonginSi, Gyeonggi-Do, 446-701, South Korea b Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads 229, 2800, Kgs. Lyngby, Denmark

A R T I C L E I N F O

A B S T R A C T

Keywords: Renewable energy forecasting Deep learning Sustainable energy policy Renewable energy scenario Techno-economic-environmental analysis Jeju island

Renewable and sustainable energy systems and policies have globally been promoted to transition from fossil fuel sources to environmentally friendly renewable energy sources such as wind power, photovoltaic energy, and fuel cells. Wind and solar energy sources are erratic and difficult to implement in renewable energy systems, therefore, circumspection is needed to implement such renewable energy systems and policies. Accordingly, this study develops an energy forecasting model with renewable energy technologies on which policy can be based, using the Korean energy policy as a case study. Deep learning-based models forecast fluctuating variation in electricity demand and generation, which are necessary in renewable energy system but not possible with conventional models. The gated recurrent unit shows the best prediction performance among the forecasting models evaluated, and is therefore selected as the base model to evaluate four different renewable energy sce­ narios. The scenarios are evaluated according to economic-environmental cost assessment. The optimal scenario uses an integrated gasification combined cycle, onshore and offshore wind farms, photovoltaic power stations, and fuel cell plants; in particular, this scenario shows the lowest economic-environmental costs, generates stable electricity for demand, and achieves a policy with 100% renewable energy. The optimal scenario is assessed by considering its strengths, weaknesses, opportunities, and threats analysis while also considering technoeconomic-environmental domestic and global energy circumstances.

1. Introduction Sustainable development and global climate change are important issues in the 21st century with regard to energy. Energy consumption is increasing by 2% per year, and overall energy generation is currently dependent on fossil fuels [1]. Anthropogenic greenhouse gas (GHG) emission due to the usage of fossil fuels, is growing considerably, causing abnormal climate across the globe, including droughts and heavy rainfall [2,3]. Moreover, it is estimated that GHG will increase by 30% in 20 years without any regulatory restriction on the use of fossil fuels [1]. Therefore, global efforts regarding renewable and sustainable energy systems and policies, including energy management, have been conducted to overcome the dependency of fossil fuel power plants and mitigate global climate change and negative environmental effects due to fossil fuels [4,5].

The Korean energy system is highly dependent on fossil fuels such as coal, oil, and liquefied natural gas, which make up 64.4% of its energy sources. Thus, Korea was ranked first with regard to increased GHG emission rates from 1990 to 2014, and sixth regarding GHG emissions by the Organization for Economic Co-operation and Development in 2015 [6,7]. As a result of the energy problem of Korea, the Korean govern­ ment has strived to reduce GHG emissions while increasing the use of renewable energy facilities and developing renewable energy systems. One of the more remarkable renewable energy systems of Korea is the use of 100% renewable energy on Jeju Island. Jeju island has been designated as a 100% renewable energy system island with the imple­ mentation of wind farms, photovoltaic power stations, and fuel cell plants [7]. Several studies investigated the feasibility of the policy and sug­ gested renewable energy systems on Jeju island. Kim et al. [8] suggested a sustainable energy management system composed of a wind turbine, a

* Corresponding author. E-mail address: [email protected] (C. Yoo). 1 The first and second authors have identical collaboration in this paper. https://doi.org/10.1016/j.rser.2020.109725 Received 26 March 2019; Received in revised form 30 December 2019; Accepted 21 January 2020 Available online 4 February 2020 1364-0321/© 2020 Elsevier Ltd. All rights reserved.

Renewable and Sustainable Energy Reviews 122 (2020) 109725

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Abbreviations ANN ARIMA ARMA CCS DNN EMD GHG GRU HSS HVDC IGCC IMFs

LSTM MAE MASE ML MLR ReLU SARIMA SWOT

artificial neural network autoregressive integrated moving average auto regressive moving average carbon capture and sequestration deep neural networks empirical mode decomposition greenhouse gas gated recurrent unit hydrogen storage system high voltage direct current integrated gasification combined cycle intrinsic mode functions

long short-term memory mean absolute error mean absolute scaled error machine learning multiple linear regression rectified linear unit seasonal autoregressive integrated moving average strengths, weaknesses, opportunities, and threats

Nomenclature Crenewable energy facility capacity of renewable energy facility (MW) Nrenewable energy facility number of renewable energy facilities $renewable energy facility economic and environmental cost of renewable energy facility ($/year)

photovoltaic panel, a battery, and an electricity converter. The hybrid system was evaluated in terms of the net present cost and the cost of electricity. Kwon et al. [9] proposed a renewable energy supply system using the wind turbine, the photovoltaic panel, and the battery. They also evaluated the system considering variations in equipment costs, wind, and solar radiation. Although these studies presented notable results, they were conducted based on a monthly measured data set, and only evaluated the operation costs. Renewable energy-related informa­ tion such as daily complex patterns of energy demand and supply, electricity generation facility costs considering the economy and envi­ ronment, and the selection of renewable energy facilities with their capacities can affect the economic and environmental feasibility of renewable energy systems [5,10,11]. Therefore, it is essential to consider the complex patterns of the renewable energy through a fore­ casting model and relate these patterns with the sustainable energy system and policy; this is because the forecasting model reflects erratic and uncertain characteristics of the energy demand and supply [11,12]. A renewable energy forecasting model with a long-term time scale (seven-days ahead) can be utilized for a feasibility study of the energy system design policy, and moreover, this model can reduce unnecessary regulatory costs while implementing renewable energy sources into the energy system [11,13,14]. We developed a forecasting model with seven-days ahead electricity demand and renewable energy generation based on deep learning techniques and domain knowledge. These results can be used to promote and guide feasible renewable energy systems and policies. This study compares and evaluates deep learning models and conventional statis­ tical models. The deep learning models include deep neural networks (DNN), long short-term memory (LSTM), and gated recurrent unit (GRU), and overcome the disadvantages of conventional statistical models such as multiple linear regression (MLR) and seasonal autore­ gressive integrated moving average (SARIMA). Comparison and evalu­ ation of the forecasting models are significant since deep learning models can have different performances depending on the properties of the data. The performances of deep learning models differ according to the forecasting time, training duration, target data, and simple or ensemble structure of the models [15,16]. However, there are several issues to consider when selecting an appropriate forecasting model. Thus, we thoroughly compare and evaluate the forecasting models using accurate numerical evaluators and confidence intervals, and select the best forecasting model for future electricity demand and renewable energy generation. We then utilize the proposed model for renewable energy scenarios for the policy design of Jeju Island to achieve their energy policy (see Section 3.1 and Appendix A). The renewable energy sources considered in this study are wind power, photovoltaic power, fuel cells, an integrated gasification combined cycle (IGCC), and a hydrogen storage system (HSS). In this study, the following four sce­ narios are compared: (1) wind power, photovoltaic power, and fuel cells,

(2) IGCC, wind power, photovoltaic power, and fuel cells, (3) wind power, photovoltaic power, fuel cells, and HSS, and (4) IGCC, wind power, photovoltaic power, fuel cells, and HSS. The renewable energy capacity for each scenario is determined by considering the electricity demand of the target region. Then, the scenarios are evaluated by eco­ nomic and environmental aspects; the cheapest renewable energy sce­ nario that produces 100% energy is assessed according to its strengths, weaknesses, opportunities, and threats (SWOT) considering techno-economic-environmental domestic and global renewable energy circumstances. The evaluated scenarios can guide the policy makers and decision-makers of Jeju Island. 2. Literature review 2.1. Renewable energy forecasting models Historical studies related to renewable energy, as summarized in Table 1, have been conducted. First is the forecasting of electricity de­ mand and renewable energy generation. The forecasting of electricity demand and renewable energy generation can provide an appropriate basis to manage and plan the energy supply and design policy [12,14]. Several studies have developed time-series statistical energy forecasting models for stable energy infra-structures to establish an exact energy plan. For example, Thatcher [17] predicted a future local electricity demand curve for Australia using a multiple linear regression (MLR) model that used local past electricity demand and weather conditions. Bianco et al. [18] presented a multiple regress model to predict annual energy consumption up to 2030 using an annual gross domestic product and expected population in Italy. Goia et al. [19] forecasted the short-term energy demand caused by heating using a functional linear regression model. Barak and Sadegh [20] suggested a hybrid autore­ gressive integrated moving average (ARIMA)-adaptive neuro-fuzzy inference system to forecast the annual electricity consumption in Table 1 A historical review of renewable energy; energy forecasting and renewable en­ ergy policies. Criterion

Description

References

Energy forecasting

Energy demand forecasting using a conventional time-series forecasting model An ARIMA-based forecasting model for renewable energy generation Electricity demand forecasting model using a neural network Ensemble machine learning models to forecast renewable energy Current and future energy policy assessment Renewable energy scenario-based future energy analysis

[17–20]

Political strategies

2

[21–25] [27–30] [31–35] [38–42] [43–46]

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Renewable and Sustainable Energy Reviews 122 (2020) 109725

Iran. Reikard [21] forecasted solar irradiance using the ARIMA model, which considered the log value of the observed solar irradiance. Palomares-Salas et al. [22] compared the performance of ARIMA and neural network model to forecast short-time intervals wind speed, and the ARIMA showed better performance. Li et al. [23] used the auto regressive moving average (ARMA) and wavelet transform to predict the wind speed for instituting a wind power system plan. Hejase and Assi [24] suggested a times-series regression with Box-Jenkins ARMA model for predicting solar radiation. Vagropoulos et al. [25] presented the seasonal autoregressive integrated moving average (SARIMA) and SARIMA model with an exogenous factor to forecast the energy gener­ ation of grid-connected photovoltaic plants. However, it is difficult to assure the forecasting accuracy of these statistical models because each model is very complex, structure selection is not easy, and prediction accuracy decreases with increasing prediction time. Currently, statistical forecasting models are unreliable, making them inappropriate as a factor in decision making [13,26]. Thus, the machine learning (ML) forecasting model, which has the ability to interpret the relationship between the input and output data without an explicit algorithm, has been used to overcome the disad­ vantages of conventional statistical forecasting models. Geem and Roper [27] proposed an artificial neural network (ANN) model to predict the overall energy demand of Korea, and this model considered the gross domestic product, population, import, and export amounts as input variables. Ekonomou [28] used an ANN to forecast the long-term energy consumption of Greece, and the ANN model was compared with a conventional model to check the superiority of the model. Kaytez et al. [29] developed least squares support vector machines to forecast the energy consumption of Turkey. They compared the performance of least squares support vector machines and the ANN model considering the specificity and sensitivity of a receiver operating characteristic analysis. Muralitharan et al. [30] suggested a neural network-based genetic al­ gorithm and a neural network-based particle swarm optimization model to forecast the electricity demand of a smart grid composed of 750 houses. Li and Shi [31] evaluated three ML forecasting models (feed forward back propagation, radial basis function, and adaptive linear element ANN) to predict the wind speed, emphasizing a method to combine forecasts from different ANN models. Chang [32] applied the radial basis function ANN to forecast the wind power for a wind energy conversion system. Troncoso et al. [33] evaluated the model perfor­ mances when predicting short-term wind speed forecasting. Ensemble models that implemented a k-nearest neighbors with local non-linear model and weighted k-nearest neighbors with local non-linear model showed the best performance among the eight regression trees-based models. Galaet al. [34] proposed a hybrid machine learning model that uses support vector regression, gradient boosted regression, and random forest regression to predict solar radiation directly related to solar energy generation. Heinermann and Kramer [35] forecasted wind power production by suggesting a heterogeneous ensemble ML model composed of a decision tree and support vector machine. However, the forecasting performance of a normal ML method is limited when using it by itself. However, this performance can be improved by combining it with other methods. In this context, deep learning is a branch of ML that models high-level abstractions and has the ability to describe a hidden high-level invariant structure and inherent features from the data. Deep learning can also be used as an effective alternative technique to overcome the limitations of conven­ tional statistical forecasting models and simple ML forecasting models. Despite these advantages of deep learning, research in this area is recent, and applications of such deep learning techniques to energy forecasting is still in the early stages [36,37].

several variables. Kim and Park [38] presented a politically motivated smart grid system in Korea considering the overall energy, energy con­ sumers, transportation, sustainable energy, and electricity services by separating the model into three future stages. Aslani et al. [39] sug­ gested an energy model evaluating Finnish renewable energy policies by analyzing the energy policy scenarios and dependency on imported energy. Scarlat et al. [40] analyzed a renewable energy policy of the European Union and determined the major energy sources that will in­ crease the use of renewable energy. He et al. [41] proposed energy policy path stages for overcoming the limitations of the Chinese sus­ tainable energy policy. Han and Baek [42] compared the capacity of global renewable energy generation with that of Korea, and suggested future political strategies and directions to increase the use of renewable energy. Mathiesen et al. [43] suggested smart renewable energy systems considering several energy scenarios, including gasified biomass, gas storage, electric vehicles, electro-fuel production, and heating systems. Cho and Kim [44] analyzed the Korean electricity supply system sce­ narios for future renewable energy sources from economic, energy se­ curity, and environmental viewpoints. Tripathi et al. [45] found a way to provide electricity to India using renewable energy based on the current renewable energy system of India. Connolly et al. [46] analyzed smart energy system scenarios for European renewable energy systems considering the energy, environment, and economics. However, in all of these analyses, the predicted renewable energy supply and total elec­ tricity demand of the countries were assumed to be non-dynamic. En­ ergy systems are complex because electricity demand and generation have intricate physical and social interactions. Therefore, both energy planning and the policy need to be facilitated through the use of deep-exact forecasting models that can be linked to the energy, econ­ omy, and environment while accurately predicting the dynamic elec­ tricity demand and generation [9,12,14,47]. 3. Energy forecasting and renewable energy scenarios The proposed framework of this study for forecasting electricity demand and renewable energy generation and analyzing renewable energy scenarios is graphically shown in Fig. 1. The first stage is implementation of a forecasting model to predict variations in electricity demand and renewable energy generation. Electricity demand and renewable energy generation via wind power and photovoltaic power were collected from 2013 to 2017 on Jeju Island and are shown in Figs. 1 (a) and Fig. 2. The demanded and supplied electricity are measured in total megawatts per day. Then, the data are decomposed through empirical mode decomposition (EMD), as shown in Fig. 1 (b), to enhance the performances of the forecasting models by constructing intrinsic mode functions (IMFs) from the nonlinear time-series data. Fig. 1 (c) shows an algorithm used in the forecasting model. The decomposed data are input into the forecasting models, and the original electricity data, which are not decomposed, are forecasted by the model. The forecasting models use a moving window method that utilizes 21 days of data for learning and forecasting electricity data for the next seven days. This window moving technique has an advantage in that it enhances the time-dependent forecasting performance of the models. As shown in Fig. 2, four years of data, from 2013 to 2016, are used as the training set, and data for 2017 are used to test the forecasting models. This study uses two statistic forecasting models—MLR and seasonal autoregressive in­ tegrated moving average (SARIMA)—and three deep learning-based forecasting models—GRU, LSTM, and DNN. The electricity demand and the generated renewable electricity have highly fluctuating char­ acteristics, which can be issues for forecasting future electricity demand and generation using only the forecasting model. Therefore, it is crucial to extract hidden patterns in the data by utilizing domain knowledge such as the EMD. The extracted domain knowledge not only reduces irrelevant information in forecasting algorithms, but also improves the forecasting accuracy [48,49]. Therefore, decomposed data for the pre­ vious 21 days are employed as the domain knowledge to forecast

2.2. Political strategies Several studies have analyzed and proposed current and future en­ ergy scenarios to achieve a renewable energy policy by considering 3

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Fig. 1. Graphical diagram of the forecasting model and renewable energy scenario analysis; (a)–(g) indicate sub-stages of the proposed framework.

electricity for the next seven days. Fig. 1 (d) shows that the forecasted electricity data model is validated compared to the observed data set with respect to the mean absolute scaled error (MASE) and mean ab­ solute error (MAE). The MASE is suitable for measuring the forecast accuracy when the data have a varied scale, and the MAE is generally used as a model evaluator, measuring differences between the forecasted and observed data [50,51]. The MASE and MAE are defined as: 0 1 MASE ¼

T B B 1X B T t¼1 B @

1 T 1

T P

MAE ¼ t¼1

jyt T

b yt j

C C jyt by t j C C T P jyt yt 1 jA

y t are the observed data and forecasted data at time t, where yt and b respectively. The second stage is the suggestion and analysis of a renewable en­ ergy scenario on Jeju Island. Based on the quantities of forecasted electricity demand and renewable energy generation in 2017, four renewable energy scenarios are suggested to supply electricity using 100% renewable energy technologies such as wind power, photovoltaic power, fuel cells, and IGCC. The suggested renewable energy scenario is graphically described in Fig. 1 (e). The different scenarios, which are explained in Section 3.4 in detail, individually suggest a feasible com­ bination of the renewable energy facilities’ capacities to satisfy timevarying electricity demands with the least cost. Economic costs in terms of construction, operation and management, and environmental costs incurred due to the emission of pollutants from energy facilities are the analysis factors considered for the scenarios, as shown in Fig. 1 (f). The scenario that has the least total economic-environmental costs

(1)

t¼2

(2)

4

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Renewable and Sustainable Energy Reviews 122 (2020) 109725

considering the global status of the energy system and market. 3.1. Current energy system status and energy policy of Jeju Island The current energy generation system of Jeju Island mainly consists of fossil fuels instead of renewable energy. Therefore, deep insights and considerations are needed to achieve 100% electricity generation by renewable energy technologies in the future. This study suggests an accurate forecasting model to design an acceptable renewable and sus­ tainable energy policy corresponding to Jeju Island’s characteristics. The details of the current energy system and energy policy of Jeju Island are discussed in Appendix A. 3.2. Forecasting model for electricity demand and renewable energy generation The renewable energy generation and total electricity demand of Jeju Island are predicted by forecasting models that combine data decomposition techniques and conventional statistical and deep learning prediction techniques. This combination of techniques results in a high performance for feature extracting and forecasting. The decomposition algorithm improves the forecasting ability of the model by providing decomposed flexible sub data from intermittent raw data. 3.2.1. Empirical mode decomposition (EMD) Empirical mode decomposition (EMD) is a widely used decomposi­ tion algorithm. EMD is fully data-driven, adaptive, and does not need predetermined transforms that depend on the selection of a specific structure. EMD decomposes non-linear and non-stationary data xðtÞ into intrinsic mode functions (IMFs) satisfying two conditions: the number of extrema and zero-crossings are equal or have a difference of one, and the mean value of the envelopes defined by the local maxima and minima is zero or close to zero. As shown in Fig. 1 (b), this study uses EMD to enhance the performance of the forecasting model, and the computed IMFs and residue are input into the forecasting model. The main algo­ rithm of EMD is described in Appendix B with Eq. (B.1). 3.2.2. Conventional statistical forecasting models The sub-series decomposed data by EMD is used as the input for the statistical and deep learning forecasting models. This study uses con­ ventional statistical forecasting models MLR, SARIMA, deep learning models DNN, LSTM, and GRU, as shown in Fig. 1 (c). The MLR predicts a dependent variable y using two or more explanatory independent vari­ ables x via linear equation fitting [52]. Additionally, SARIMA is the extended ARIMA model and improves the forecasting ability by removing seasonal variation trends by considering the differences. The details of MLR and SARIMA are described in Appendix C. 3.2.3. Deep learning-based forecasting models Deep learning models are recent machine learning methods, but have rarely been implemented in the field of energy forecasting. In this study, such models are used with EMD for energy forecasting to overcome the weaknesses of conventional statistical forecasting models. Among the several deep learning models, this study utilizes DNN as a fundamental deep learning model and LSTM and GRU to consider the time-series characteristics of the data. DNN uses a multi-layer structure consisting of one input layer, one output layer, and several hidden layers based on a hierarchical model. The rectified linear unit (ReLU) and dropout are used to overcome the vanishing gradient problem and over-fitting. The ReLU evades gradient vanishing by converting negative inputs to zero, and the dropout randomly drops units from the hidden layers while computing activations in the forward pass and updating weights in the backward pass [53]. LSTM and GRU are the improved recent versions of conventional deep recurrent neural networks (deep RNN). Deep RNN has three major disadvantages: training takes a long time, deep RNN is not able to learn

Fig. 2. Electricity data of Jeju island from 2013 to 2017; (a) electricity de­ mand, (b) wind electricity generation, and (c) photovoltaic elec­ tricity generation.

among the scenarios is selected as the optimal scenario to provide guidance for sustainable energy policy design. Finally, the optimal scenario is assessed using a SWOT analysis to overcome various weak­ nesses and threats, as well as maximize the strengths and opportunities. The techno-economic-environmental SWOT analysis includes domesticinternal and global-external assessments, as shown in Fig. 1 (g). The internal assessment illustrates the strengths and weaknesses of the optimal scenario considering the energy circumstances of Korea, and the external assessment demonstrates the opportunities and threats by 5

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suggested. The information from the renewable energy facilities is summarized in Table F1. The environmental costs, shown in Table F2, are the environmental marginal damage costs consid­ ering climate change damage [61–63]. All costs are converted to the 2017 equivalent dollar using the consumer price index.

long-term patterns, and information related to the initial data gradually fades away. Therefore, LSTM and GRU were developed to solve these problems by implementing deep and long-term memory cells in their structures, similar to their applications in natural language processing [54]. Despite these advantages of deep learning, their use in energy forecasting is recent and studies related to energy forecasting are still in the early stages [36]. The details of LSTM and GRU are provided in Appendix D.

The number of renewable energy facilities in each scenario is calculated by minimizing the economic and environmental total costs. For instance, the objective function for calculating economic and envi­ ronmental costs in the case of scenario 1 is expressed by Eq. (3).

3.3. Renewable energy technologies

f ¼ Nonshore wind farm � $onshore wind farm þ Noffshore wind farm � $offshore wind farm þNphotovoltaic power station � $photovoltaic power station þ Nfuel cell plants � $fuel cell plants

Based on the proposed policy that does not utilize fossil fuels or other methods resulting in the emission of carbon dioxide, this study uses wind farms, photovoltaic power stations, and fuel cells. Additional sustainable energy technologies, IGCC and HSS, are used to assess renewable energy scenarios in this study to supply stable electricity not affected by variations in the weather or season. Detailed descriptions of the renewable and sustainable energy technologies are provided in Appendix E.

Nonshore wind farm � Conshore wind farm þ Noffshore wind farm � Coffshore wind famr � 8 � Nphotovoltaic power station � Cphotovoltaic power station

(3) (4)

Nonshore wind farm � Conshore wind farm þ Noffshore wind farm � Coffshore wind farm þNphotovoltaic power station � Cphotovoltaic power station þ Nfuel cell plants � Cfuel cell plants � Necessary capacity

3.4. Description of the renewable energy scenarios

(5) Here, Nrenewable energy facility is the number of renewable energy facilities (e.g., Nonshore wind farm is the number of onshore wind farm), $renewable energy facility is the economic and environmental cost of the renewable energy facility, and Crenewable energy facility is the capacity of the renewable energy facility. Note that this study uses the nominal capacity suggested by EIA [60] as summarized in Table F1. Eqs. (4) and (5) are constraint functions on the capacity determination of the renewable energy facilities. The first constraint is formulated based on the expan­ sion plan of renewable energy facilities of Jeju Island, where the ca­ pacity of the wind farms is eight times that of the photovoltaic power stations. The second constraint is that the total capacity of renewable energy technologies should be higher than the necessary capacity, which is 120% of the forecasted electricity demand. In addition, the following boundary conditions of the wind farms are applied:

The four scenarios in this study are introduced to help inform sus­ tainable energy policy design and decisions for Jeju Island (Fig. 1 (e)). The prepared scenarios are: (1) Scenario 1: offshore wind farms, onshore wind farms, photovol­ taic power stations, and fuel cell plants; base scenario that is based on the current sustainable energy plan of Jeju Island. (2) Scenario 2: offshore wind farms, onshore wind farms, photovol­ taic power stations, fuel cell plants, and IGCC; use of the addi­ tional IGCC technology to supply stable electricity. (3) Scenario 3: offshore wind farms, onshore wind farms, photovol­ taic power stations, fuel cell plants, and HSS; use of the additional HSS to store surplus generated electricity and supply the stored electricity. (4) Scenario 4: offshore wind farms, onshore wind farms, photovol­ taic power stations, fuel cell plants, IGCC, and HSS; use of addi­ tional sustainable energy technology and an energy storage system.

0 � Nonshore wind farm � 2

(6)

1 � Noffshore wind farm � 5

(7)

Determining the maximum numbers of onshore and offshore wind farms is one goal of the policy, and the minimum numbers of onshore and offshore wind farms are based on the ongoing construction projects on Jeju Island. The HSS is used in scenarios 3 and 4, and is determined using the extended-power cascade analysis method, which designs a hybrid power system with hydrogen storage. The component costs of the HSS are summarized in Table F1 under the assumptions that the power losses of conversion and transfer are negligible and the efficiencies of the storage system components are 100% [4].

The capacity of the renewable energy technologies in each scenario is determined under the following five assumptions: (1) The lacking electricity that should be supplied by the renewable energy is calculated as the forecasted total electricity demand minus the forecasted electricity generation by renewable energy technologies (wind power and photovoltaic power). (2) The necessary capacity of renewable energy facilities is 120% of the forecasted demand, where this percentage considers the power margin recommended by the Ministry of Trade, Industry, and Energy in Korea. (3) As previously mentioned, the energy supplies of wind power and photovoltaic power fluctuate based on the weather and seasonal conditions. Thus, in these scenarios, the electricity generation by wind farms and photovoltaic power stations is estimated by considering the variations of electricity over four years. (4) The lifespan of the energy facilities are 30 years for IGCC, 20 years for fuel cells, 25 years for onshore and offshore wind farms, and 15 years for photovoltaic power stations [57–59]. (5) The economic costs suggested by EIA [60] and environmental costs suggested by Refs. [61–63] of each renewable energy technology are used. EIA [60] suggested economical costs considering capital costs and operating and management (O&M) costs according to the nominal capacity of the power plants. In addition, pollutant emissions from each power plant are

4. Results and discussion 4.1. Forecasting of electricity demand and electricity generation by renewable energy Fig. 3 shows representative results of data decomposition of wind electricity generation using the EMD. The numbers of IMFs are 7, 10, and 8 for total electricity demand, wind electricity generation, and photovoltaic electricity generation, respectively. The number of IMFs is the lowest when the total electricity demand data are used; on the other hand, this number is the highest when of the decomposed wind elec­ tricity generation data are used. In addition, IMF signals appear with higher frequency in the cases of wind electricity generation compared to those of electricity demand and photovoltaic electricity generation. The higher frequencies of IMFs mainly reflect the random noise information of the original data, and the number of IMFs is increased according to the 6

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Renewable and Sustainable Energy Reviews 122 (2020) 109725

Fig. 3. Original wind electricity generation data, and ten IMFs and one residue, which are computed by EMD-based decomposition of the wind electricity gener­ ation data.

amount of noise. This result suggests that wind energy has the highest noise intensity among the observed data sets. The reason for such erratic wind power is the climate and geographical characteristics of Jeju Island and Korea. Wind speed, which is directly correlated with the amount of wind electricity generation, is relatively low in the summer compared to the spring, fall, and winter in Korea, while wind speed in the winter is the highest [64]. The reasons for wind speed variation include the dif­ ferences in temperature between the land and sea and the wind shear exponent. The wind speeds from the Pacific in the summer are much lower than those from Siberia in the winter, because the temperature is much more different between the land and the sea during the winter. In addition, the wind shear exponent is relatively high in the summer [65]. Therefore, a robust forecasting model should be implemented to predict

the large fluctuations and erratic energy trends according to time, weather, and season. In this context, this study utilizes a deep learning-based forecasting model to capture the characteristics of elec­ tricity demand and generation, as well as predict the electricity gener­ ation with robust performance. Table 2 shows the electricity demand and renewable energy gener­ ation forecasting results of the statistical models and the deep learning models based on MASE and MAE as the forecasting models evaluators. GRU shows the best forecasting performance of the electricity demand, wind electricity generation, and photovoltaic electricity generation when evaluated by the MASE. On the other hand, the statistical models show better forecasting performance than the deep learning models with respect to the MAE. This result is explained by the characteristics of the 7

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Table 2 Forecasting results of the electricity demand and renewable energy generation; the best forecasting model is indicated with bold font. Evaluator MASE

Electricity demand Wind electricity generation Photovoltaic electricity generation

MAE

Electricity demand Wind electricity generation Photovoltaic electricity generation

Training Test Training Test Training Test Training Test Training Test Training Test

model evaluators. Although the MAE is generally used to measure the ability of a forecasting model, it is a suitable model performance eval­ uator when forecasted and measured data have a uniform distribution [50]. Wind power and photovoltaic power electricity generation have time-series attributes that continually increase; this is because Jeju Is­ land actively constructs wind farms and photovoltaic power stations to generate 100% of their energy from renewable sources. Furthermore, as shown in the results of the data decomposition step, weather and sea­ sonal conditions disturb the efficiency of renewable power generation and result in highly erratic data. Fig. 4 (a)-(c) verify the appropriateness of a low MASE value for GRU regarding the forecasted wind electricity generation, since GRU fore­ casts the wind electricity generation while reflecting data variation. However, SARIAM and MLR only follow trend found in the data set. MASE is applicable for evaluating time-series data, and GRU shows the lowest MASE value among the conventional statistical models and deep learning models. Additionally, 95% confidence intervals for each fore­ casting model are depicted as the red area in Fig. 4 (a)–(c). The confi­ dence intervals of the GRU-based forecasting model have a similar trend of increasing and decreasing with the observed wind electricity gener­ ation, thus, GRU has a relatively lower number of outliers compared to the other forecasting models. Here, outliers represent the observations that are not included within the 95% confidence intervals, as depicted by the forecasting models [66]. On the other hand, the confidence in­ tervals by SARIMA and MLR have relatively small widths since they roughly follow the trend of wind electricity generation. SARIMA and MLR have limitations in accurately reflecting the highly varying elec­ tricity data. Therefore, it is inferred that the GRU outperforms the forecast time-series data. Fig. 5 (a)-(c) show forecasted electricity values using GRU for the electricity demand, wind, and photovoltaic electricity generation during 2017. The varied time-series trends of the forecasted electricity values for demand and generation using the GRU are similar to those for the observed data set. Furthermore, the observed electricity demand and the generated renewable energy are mostly located within the 95% confidence intervals of the GRU-based forecasting models. It is essential to detect the fluctuating trends of electricity generation via renewable energy technologies to provide insight into the energy char­ acteristics and design of a realistic energy policy. We select GRU as the optimal electricity forecasting model because it reflects the time-series and time-varied characteristics of electricity demand and generation. Thus, the GRU-based forecasted electricity demand, wind electricity generation, and photovoltaic electricity generation of 2017 are used to suggest and analyze the renewable energy scenario in Jeju Island.

GRU

LSTM

DNN

SARIMA

MLR

1.12 1.11 0.19 1.76 0.47 1.80 253.80 337.96 101.01 910.31 21.845 159.52

1.35 2.03 0.50 1.85 0.38 2.03 269.92 447.86 210.97 1022.1 18.482 166.62

1.63 1.59 1.10 2.72 0.55 1.93 398.59 443.42 321.11 915.76 24.911 162.46

1.23 1.23 15.6 6.48 7.54 7.75 449.91 535.48 583.37 871.27 59.408 135.78

2.05 1.90 8.01 7.17 8.46 8.67 397.41 437.53 539.57 795.83 54.765 127.08

wind farm, a photovoltaic power station, and a fuel cell plant. As shown in Fig. 6 (a), the number of renewable energy facilities should supply at least 120% of the forecasted electricity demand. In total, 0 onshore wind farms, 2 offshore wind farms, 5 photovoltaic power stations, and 68 fuel cell plants are used in scenario 1. The characteristics of the highly erratic and fluctuating electricity generation are observed due to wind elec­ tricity generation. Accounting for these fluctuations, 52% of the elec­ tricity in total is generated from wind power. Relatively low electricity generation is observed for the summer season, from 152 days to 243 days in Fig. 6 (a), due to low wind. However, energy demand during the summer is increased due to high temperatures and the use of air con­ ditioning. Therefore, the incorporation of wind farms should be care­ fully considered. The high capacity of renewable energy facilities is estimated in scenario 1 to satisfy the electricity demand of Jeju Island. Highly fluctuating wind electricity generation induces high capacities for the facilities by overestimating the number of other facilities. Thus, surplus electricity is generated during other seasons. These results indicate the necessity of a policy design with careful consideration of renewable energy and a thorough understanding of the renewable en­ ergy facility characteristics. In this context, this study intends to inform sustainable energy policies and guide policy makers by suggesting and analyzing feasible and reliable renewable energy scenarios while implementing other renewable energy facilities. Fig. 6 (b) shows the results of scenario 2, which uses additional IGCC sustainable energy facilities. Scenario 2 generates electricity from 1 IGCC, 1 onshore wind farm, 1 offshore wind farm, 3 photovoltaic power stations, and 21 fuel cell plants. Compared to the base scenario in Fig. 6 (a), the IGCC generates stable electricity and minimizes the surplus electricity by simultaneously decreasing unnecessary renewable energy facilities. Stable electricity generation by the IGCC decreases the total capacity of renewable energy facilities from 1550 MW to 1290 MW compared to the current policy. The IGCC generates 40% of the elec­ tricity, and wind farms, which are the main renewable energy sources of the base scenario, generate 38%. This electricity generation ratio means that the dependency on wind energy is decreased from 52% to 38%, and electricity generation is more stable as the seasons change. By dimin­ ishing the capacity of total facilities and wind farms, Fig. 6 (b) shows that the intensity of surplus electricity is dramatically decreased, and scenario 2 is expected to reduce operation costs. Therefore, utilization of an additional sustainable energy facility can be an effective policy so­ lution to achieve a 100% sustainable energy system with economic benefits. Scenario 3, which uses HSS to store surplus electricity and then supply that electricity when needed, is shown in Fig. 6 (c). The number of estimated facilities is 2 onshore wind farms, 1 offshore wind farm, 3 photovoltaic power stations, and 63 fuel cell plants. A notable result is that the 85 MW HSS is used in scenario 3. The HSS mainly furnishes saved electricity for summer from day 152 to day 243. This helps pre­ vent overestimation of the renewable energy capacity, as well as generate and provide stable electricity during the summer. Thus, the total capacity, including the facilities and electricity storage in scenario

4.2. Economic-environmental analysis of feasible renewable energy scenarios Fig. 6 (a)-(d) show the electricity generation through renewable energy facilities according to the suggested renewable energy scenario in Jeju Island during 2017. Scenario 1, which is based on the current policy of Jeju Island, is composed of an onshore wind farm, an offshore 8

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Renewable and Sustainable Energy Reviews 122 (2020) 109725

Fig. 4. Forecasted wind electricity generation and comparison of the observed data and confidence intervals by using (a) GRU, (b) SARIMA, and (c) MLR; the days from 1461 days to 1825 days represent the year 2017.

Fig. 5. Results of GRU-based forecasting model and confidence intervals for (a) electricity demand, (b) wind electricity generation, and (c) photovoltaic elec­ tricity generation; the days from 1461 days to 1825 days represent the year 2017.

3, is diminished from 1550 MW to 1375 MW compared to scenario 1. The HSS has a low capacity despite its benefit of preventing capacity overestimation of renewable energy facilities and providing stable electricity. The reason for the low HSS capacity is the high price com­ ponents of the system. The polymer electrolyte membrane fuel cell, also called a proton exchange membrane fuel cell, is utilized in the HSS. The polymer electrolyte membrane fuel cell is a widely commercialized type of fuel cell due to its quiet operation, diverse flexible construction and application, and effective, cleaner chemical-electrical energy

conversion. Nonetheless, it is expensive and has short durability. The dependency of a platinum catalyst, frail fabrication of the membrane, and bipolar plate materials are the main obstacles of fuel cell utilization in renewable energy systems [55]. Although the components of the HSS are expensive, the storage system’s small capacity reduces the amount of unnecessary electricity generation. If the economic problems of the HSS can be overcome, HSS will be useful in Jeju Island’s sustainable energy 9

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Renewable and Sustainable Energy Reviews 122 (2020) 109725

Fig. 6. Results of the renewable energy scenarios, including the detailed capacity and ratio of each renewable energy facility, and variations of electricity generation for 2017; (a) scenario 1, (b) scenario 2, (c) scenario 3, and (d) scenario 4.

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Renewable and Sustainable Energy Reviews 122 (2020) 109725

plans and policy. Both the IGCC and the HSS are utilized in scenario 4, which is shown in Fig. 6 (d). There is 1 IGCC, 1 onshore wind farm, 1 offshore wind farm, 3 photovoltaic power stations, 21 fuel cell plants, and no use of the HSS. Contrary to our expectation, the HSS is not implemented in scenario 4 due to its inherent high costs and sufficient stable electricity generation by the IGCC. The renewable energy facilities of scenario 4 correspond to those of scenario 2. Table 3 shows the capacity of the facilities and the economicenvironmental costs of each scenario. Scenario 1, estimated using the current policy of Jeju Island, shows the highest capacity and costs. These high values result mainly from wind electricity, which interrupts effective electricity generation. Therefore, scenarios 2, 3, and 4 are suggested to inform effective electricity generation systems and the related policies. Scenario 2 used the IGCC as the main electricity gen­ eration facility. The IGCC uses coal, but scenario 2 still maintains low environmental costs for operation and maintains consistent renewable energy. The reason for these low environmental costs is the combination of mitigation technology CCS and IGCC; it uses a water gas shift reactor system and a two-stage acid gas removal system to capture CO2 from the syngas before combustion [60]. This IGCC-CCS effectively prevents pollutant emission and reduces environmental costs. Moreover, the IGCC decreases the required electricity capacity of the facilities and decreases the costs. Compared to the base scenario, scenario 2 decreases the required capacity of the facilities by 16.74%, economic costs by 33.94%, environmental costs by 52.91%, and total economic-environmental costs by 36.16%. Thus, scenario 2 saves $326, 490,000 dollars per year while satisfying sufficient electricity generation for electricity consumption in Jeju Island. The effect of scenario 2 is also highlighted in Fig. 7 considering the capacity and cost aspects. Scenario 3, which uses an HSS to save and provide surplus electricity, decreases the required capacity of the facilities by 11.29% and the total economic-environmental costs by 8.26%. Though scenario 3 reduces the economic-environmental costs compared to the base scenario, the costs are higher than those of scenario 2. To summarize the results of the four scenarios, scenario 2 is the best scenario to guide policy and attain a renewable energy generation sys­ tem on Jeju Island. In addition, IGCC is an effective renewable energy facility selection with respect to economic and environmental aspects. We additionally assess the optimal scenario by conducting a technoeconomic-environmental SWOT analysis to evaluate the feasibility and reliability of applying it to Jeju Island’s energy system according to the renewable energy conditions of both Korea and the world.

4.3. SWOT analysis considering techno-economic-environmental aspects for the optimal renewable energy scenario The strengths represent the available resources for enhancing the performance of the scenario. The main strength of the optimal scenario is its application of IGCC. IGCC is a potential clean coal power genera­ tion process that has two main characteristics—sustainability and flex­ ibility. First, coal is mainly used in the IGCC. Globally, the reserve of coal is 59.9%, oil is 23.4%, and natural gas is 16.7%. In addition, the reserves-to-production ratio, or the amount of remaining fuel expressed over time, is the longest for coal (109), followed by natural gas (55.7) and oil (52.9) [67]. Therefore, the abundant reserve of coal supports the suitability of IGCC. Second, IGCC is operated under flexible multiple feeds and generates multiple products. The IGCC uses biomass from agriculture, forestry, and energy crops; petroleum residues, which include coke, asphalt, and heavy oil; as well as coal such as peat, lignite, and anthracite as fuel [56]. The diverse feeds lend flexibility to the IGCC. These two technical characteristics enhance the benefits attained by the IGCC. Further, the stable and steady electricity generation of the IGCC is better than the erratic and fluctuating electricity generation that results from wind and photovoltaic power. Thus, the optimal scenario has many advantages with regard to raw materials, stable electricity, and low total economic-environmental costs. However, IGCC has weaknesses that can diminish its competitive advantages and efficiency. First, the IGCC emits pollutants. Although the IGCC is a clean power process and is combined with the CCS to prevent CO2 emission, the IGCC is a fossil fuel-based power plant. Thus, the IGCC with CCS emits SO2, NOX, and CO2, which violates the zero carbon emission objective of Jeju Island’s energy policy. Second, this scenario does not utilize the abundant wind source of Jeju Island. In its 1290 MW capacity, 38% of the electricity is supplied by onshore and offshore wind farms. Jeju Island has been evaluated as the best location in Korea for operating wind farms due to its abundant and strong wind speeds [68]. Despite the geographical advantages of wind, non-stable electricity generation from wind farms prevents the effective utilization of wind power on Jeju Island. New technologies to increase the efficiency of wind turbine generators may overcome this limitation of wind power. External policies are additional contributing opportunities for sus­ tainable energy in Korea. First, the Korean ministry announced an IGCC construction schedule considering long-term electricity supply and de­ mand. Based on this construction plan, a 300 MW IGCC facility was constructed at 2016 in Taean, Korea. Moreover, an additional plan was established in 2015 to build an IGCC plant in Namhae, with expected completion in 2022. Thus, a positive prospective of the IGCC is expected in Korea. Second, the Moon administration emphasizes phasing out nuclear power and closing old coal power plants to assure public safety and promote environmentally friendly practices. According to the newly announced policy, the 40-year Kori-1 nuclear power reactor was retired, and five 30-year old coal power plants will be closed. Therefore, alter­ native feasible clean power plants are needed to compensate for the closure of conventional power plants and supply stable electricity gen­ eration. This may be an opportunity to develop an IGCC plant as well as other sustainable energy technologies. Threats, which are outside factors, affect the energy supply and de­ mand and cause problems. Coal, which is the basic fuel used to operate the IGCC plant, is mostly imported. Only 1.3% of coal is mined in Korea, and 98.7%, which is 113.5 million tons of coal, is imported from Austria, Indonesia, Russia, and Canada [44]. Although coal is mined in several regions throughout the world and has the advantage of energy security compared to oil, which is provided from limited regions such as the Middle East, the high dependency on coal imports may threaten the dependability of the IGCC operation. Another threat to the IGCC on Jeju Island is the shale gas industry. Shale gas production is increasing and will soon constitute half of the total amount of natural gas; thus, the price of shale gas is continuously decreasing. Furthermore, the Korean government plans to utilize shale gas by promoting manpower and

Table 3 Comparison of the detailed capacity and total annual costs for each scenario. Onshore wind farms (MW) Offshore wind farms (MW) Photovoltaic power stations (MW) Fuel cell plants (MW) IGCC (MW) HSS (MW) Total capacity (MW) Economic costs ($/year) Environmental costs ($/year) Total costs ($/year)

Scenario 1

Scenario 2

Scenario 3

Scenario 4

–

100

200

100

800

400

400

400

100

60

60

60

650

210

630

210

– – 1550

520 – 1290

– 85 1375

520 – 1290

797,461,120

526,797,936

726,155,524

526,797,936

105,510,000

49,683,000

102,260,000

49,683,000

902,970,000

576,480,000

828,420,000

576,480,000

11

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Renewable and Sustainable Energy Reviews 122 (2020) 109725

Fig. 7. Effect of the optimal renewable energy scenario with respect to the diminished total capacity of electricity generation facilities and the decreased economicenvironmental costs.

Research & Development [69]. Interest in shale gas may restrain the expansion of IGCC and renewable energy technologies in Korea. The SWOT analysis results of the optimal scenario for Jeju Island with a 100% renewable energy system are summarized in Table 4.

forecasting model showed the best performance in terms of the MASE when compared to other deep learning models and statistical models. Moreover, compared to the other models, the GRU has the ability to reflect and forecast data that rapidly fluctuate over time. The suggested forecasting model provides feasible grounds for establishing appropriate energy policies. Black out, which occurs when electricity generation is insufficient, and surplus electricity, which is when electricity generation exceeds the capacity of the system, can be prevented via robust policies based on an accurate energy forecasting model. Based on the forecasted electricity demand and generation by GRU, four feasible renewable energy scenarios consisting of wind farms, photovoltaic power stations, fuel cell plants, IGCC, and/or HSS are suggested and evaluated according to various economic and environ­ mental aspects. Scenario 2, which uses the IGCC, wind farms, photo­ voltaic power stations, and fuel cell plants, was determined as the best scenario. The results illustrate that scenario 2 generates stable electricity by consistently providing 520 MW electricity from a IGCC with CCS system, 100 MW electricity from onshore wind farms, 400 MW elec­ tricity from offshore wind farms, 60 MW electricity from photovoltaic power stations, and 210 MW electricity from fuel cell plants. Scenario 2 also reduces the total economic-environmental costs by 36.16% compared to the existing policy of Jeju Island. The SWOT assessment explains techno-economic-environmental circumstances to expand the suggested renewable energy technologies. Additionally, it can be extended to analyze scenarios in light of different system aspects such as robust carbon emission reduction. Therefore, this approach can assess and establish other renewable energy-related sys­ tems and policy implementations. Consequently, through sequential forecasting, renewable energy scenarios, and SWOT assessment, this study provides guidelines for the design of renewable energy systems and policies by considering time-varying energy trends, specific

5. Conclusions We suggested and analyzed various renewable energy scenarios to adhere to the objectives of Jeju Island to attain 100% sustainable environmental electricity generation and inform policy. We developed a deep learning-based forecasting model, which is seldom used for energy forecasting, to overcome the performance limitations of conventional forecasting models and enhance the model performance. The GRU Table 4 The summarized SWOT results of the optimal renewable energy scenario of Jeju Island. Strengths

Weaknesses

- Sustainability and flexibility of the IGCC (technical) - Stable and steady electricity generation not affected by seasonal and weather conditions (technical) - Reduction of total economicenvironmental costs (economic and environmental) Opportunities

- Difficult to maintain zero carbon emission (technical and environmental) - Lacking utilization of abundant wind sources of Jeju Island (technical)

- Vitalization of IGCC use in Korea (technical) - Alternative and effective energy source to nuclear power plants and old coal power plants (technical and economic)

- High dependency on fuel import (economic) - Rise of shale gas potential in the world (economic)

Threats

12

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Renewable and Sustainable Energy Reviews 122 (2020) 109725

characteristics of renewable energy technologies, and diagnosis of techno-economic-environmental domestic and global energy trends.

Writing - review & editing. ChangKyoo Yoo: Conceptualization, Vali­ dation, Supervision.

CRediT authorship contribution statement

Acknowledgements

KiJeon Nam: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing - original draft, Writing - review & editing, Visualization. Soonho Hwangbo: Conceptualization, Method­ ology, Software, Formal analysis, Resources, Writing - original draft,

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (no. 2017R1E1A1A03070713).

Appendix A A. Current energy system status and energy policy of Jeju island Jeju Island, located in the south sea of Korea, announced a sustainable energy policy that replaces conventional fossil fuel power plants with sustainable energy technologies such as wind power, photovoltaic power, and fuel cells. The current energy system of Jeju Island generated only 22.29% of its electricity through onshore wind farms and photovoltaic power stations. The ultimate objective of the current policy is to achieve 100% sustainable energy [1]. The proportions of electricity generation facilities and the location of current power systems are respectively shown in Figs A1 (a) and (b). In total, 42.17% of electricity is supplied by high voltage direct current (HVDC), 35.54% of electricity is generated by thermal power plants, and 22.29% of electricity is generated by renewable energy technologies. Among the renewable energy sources constituting 22.29% of total electricity generation, 80.90% and 19.10% of the electricity is generated by onshore wind farms and photovoltaic power stations, respectively [1]. The first objective of this sustainable energy policy is the installation of wind farms to utilize the wind energy sources of Jeju Island. Unfortunately, predominately focusing on wind farms has resulted in an inescapable dependency on fossil fuel energy sources. Therefore, a 700 MW HVDC was installed and has been used to supply electricity to Jeju Island; the facilities are located north of the island and receive their electricity from inland Korea. The main electricity generation facilities on Jeju Island are thermal power plants, which supply 61.45% of the electricity (not considering transmitted electricity). These percentages show that the current electricity generation is still highly dependent on thermoelectric power plants. Therefore, careful and detailed consideration of new renewable energy centers is needed to achieve 100% sustainable energy.

13

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Renewable and Sustainable Energy Reviews 122 (2020) 109725

Fig. A.1. Energy generation system of Jeju Island. (a) Ratio of electricity generation and (b) location of electricity generation facilities.

The objective of this policy is to provide a renewable energy system composed of 2 GW offshore wind farms, 300 MW onshore wind farms, 100 MW photovoltaic power stations, and 520 MW fuel cell plants. Wind power is the main source of energy in this policy, and has fluctuating electricity generation characteristics that interrupt sustainable and stable electricity supply. Therefore, the energy policy should carefully consider the char­ acteristics of Jeju Island’s climate as well as efficient electricity generation technologies. To achieve 100% renewable energy technologies for elec­ tricity, we suggest an energy forecasting model that considers a time-varying electricity trend and feasible renewable energy scenarios to design an acceptable sustainable energy policy for Jeju Island. B. Empirical mode decomposition (EMD) The main algorithm of EMD is as follows [2–4].

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Renewable and Sustainable Energy Reviews 122 (2020) 109725

(1) Identify the local maxima and minima of the data xðtÞ. Then, connect all the local maxima with a cubic spline curve to produce the upper envelope. Calculate the difference function hðtÞ between xðtÞ and the mean value of the upper and lower envelopes mðtÞ. (2) hk ðtÞ, which is the difference function at iteration k, shows a zero mean, so hk ðtÞ is then designated as the first IMF1 ðtÞ. Obtain the residue r1 ðtÞ by subtracting IMF1 ðtÞ from xðtÞ. Consider r1 ðtÞ as the new original data. Next, calculate IMF2 ðtÞ, since r1 ðtÞ includes information from a longer period. Iterate the calculation of rn ðtÞ until rn ðtÞ becomes a monotonic function from which IMFðtÞ can no longer be extracted. (3) Finally, the original data xðtÞ is decomposed into the residue rn ðtÞ and the sum of IMF1 ðtÞ to IMFn ðtÞ as in Eq. (B.1). n X

xðtÞ ¼

(B.1)

IMFi ðtÞ þ rn ðtÞ i¼1

C. Conventional statistical forecasting models The general form of the MLR is Eq. (C.1). (C.1)

y ¼ β 0 þ β 1 x1 þ β 2 x2 þ ⋯ þ β n xn

here, βi is the corresponding regression coefficient estimated by the least squares method. The SARIMA is generally represented by SARIMA (p,d,q)(P,D,Q)(s) where p is the autoregressive order, d is the differencing level, q is the moving average order, P is the seasonal autoregressive notation, D is the seasonal integrated notation, Q is the moving average notation, and s is the length of seasonal period. The Box-Jenkins methodology is utilized in the SARIMA model development and consists of four steps: identification, estimation, diagnostic checking, and forecasting. A detailed model description with mathematical equations can be found in Jeong et al. [5] and Vagropoulos et al. [6]. D. Deep learning-based forecasting models LSTM has memory block units located inside recurrent hidden layers. The units have memory cells that save temporal information and gates that input, output, and forget information as it flows through the system. The input gate controls input data into the memory cell, the output gate controls output data into the next network, and the forget gate controls the internal state of the memory cell by preventing continuous input streams through adaptively forgetting or resetting the memory of the cell [7,8]. The equations of the LSTM transition are expressed in Eqs. (D.1)-(D.6): � it ¼ σ W ðiÞ xt þ U ðiÞ ht 1 þ bðiÞ (D.1) ft ¼ σ W ðf Þ xt þ U ðf Þ ht ot ¼ σ ðW ðoÞ xt þ U ðoÞ ht

1

�

(D.2)

þ bðoÞ Þ

(D.3)

þ bðf Þ

1

ut ¼ tanhðW ðuÞ xt þ U ðuÞ ht ct ¼ it ∘ut þ ft ∘ct

1

(D.4)

þ bðuÞ Þ

(D.5)

1

(D.6)

ht ¼ ot ∘tanhðct Þ

where it , ft , and ot are the input, forget, and output gates, respectively, ct is the memory cell, ht is the output of hidden state, xt is the input at the current time, W, U and b are the parameter matrices and vector, σ is the logistic sigmoid function, and ∘ is elementwise multiplication. GRU, proposed by Cho et al. [9], is an alternative technique to LSTM. The main difference between GRU and LSTM is that a single gating unit in GRU simultaneously controls the forgetting factor and the decision to update the state unit; a separate memory cell therefore does not exist in GRU [10]. GRU is expressed by the following Eqs. (D.7)-(D.10) rt ¼ σðW ðrÞ xt þ U ðrÞ ht

1

þ bðrÞ Þ

(D.7)

zt ¼ σðW ðzÞ xt þ U ðzÞ ht

1

þ bðzÞ Þ

(D.8)

h~t ¼ tanh W ðhÞ xt þ U ðhÞ ðrt ∘ ht 1 Þ þ bðhÞ ht ¼ zt ∘ht

1

þ ð1

�

(D.9) (D.10)

zt Þ∘~ ht

~t is the candidate output of hidden state. To balance the previous output of hidden state ht and the where rt is the reset gate, zt is the update gate, and h ~t , the input and forget gates are combined into the update gate zt . Then, the reset gate rt controls the previous state candidate output of hidden state h

[11].

E. Descriptions of renewable energy technologies The wind farms receive wind power through the rotor and gearbox, and convert mechanical power into electrical power using a power converter and transformer. Although wind power is clean energy with no pollutant emission, electricity generated from wind turbines is highly erratic and fluctuates, making it difficult to implement wind farms into energy system plans [12]. Photovoltaic power stations consist of many cells connected in 15

Renewable and Sustainable Energy Reviews 122 (2020) 109725

K. Nam et al.

series and parallel that convert solar power into electrical power. Similar to the wind farms, the photovoltaic power stations can reduce energy consumption of fossil fuels and mitigate air pollution. However, photovoltaic power stations are dependent on the weather [13,14]. The fuel cell plants, composed of a fuel electrode, an oxidant electrode, and an electrolyte, produce energy from hydrogen oxidization. These are regarded as flexible chemical-to-electrical energy converters with efficient and clean advantages. Despite the advantages, the drawback of fuel cells is the high cost of equipment including catalysts, membranes, and bipolar plates [15]. The IGCC, evaluated as a clean coal power plant, generates energy from coal using a combined cycle. The combined cycle consists of a gas turbine and a steam turbine. The gas turbine combusts syngas by combing it with coal, a water slurry, and oxygen under high temperatures, and the steam turbine produces electricity using the high pressure stream generated by excess heat from the syngas combustion. Compared to a conventional pulverized coal combustion process, the advantages of IGCC are that it is associated with 10–30% pollutant emission at equivalent power generation, 30% less water use, lower fused slag production, higher efficiency, and higher energy output. In addition, carbon capture and sequestration (CCS) can be adopted into IGCC to mitigate CO2 emission [16,17]. The HSS, which is generally used as an energy storage system, stores immoderate generated electricity in a hydrogen storage tank via deionization and electrolysis. When generated electricity is lower than the electricity demand, the HSS delivers saved energy to a fuel cell that produces energy and water from the saved hydrogen. The high energy density of the hydrogen is one advantage of the HSS [12,18]. F. Economic and environmental cost

Table F.1 Characteristics of renewable energy facilities including cost information and environmental impacts [18,19]. Facility

Fuel

Nominal capacity (kW)

Nominal heat rate (Btu/kWh)

Capital cost ($">$/kW)

O&">&M ($">$/kW)

SO2 (lb/ MMBtu)

NOX (lb/ MMBtu)

CO2 (lb/ MMBtu)

Description

60 wind turbine generators, each with a rated capacity of 1.5 MW 80 wind turbine generators, each with a rated capacity of 5 MW 40 half-megawatt building blocks, each block consisting of groups of PV modules connected to a 500 KW AC inverter Multiple phosphoric acid fuel cell units, each with a power output of 400 kW, for a total output of 10 MW Two combustion turbine (60 Hz machines rated at 255MVA with an 18 kV output) and one steam turbine (60 Hz machines rated at 333MVA with 18 kV output) Components of the hydrogen storage system to store and provide electricity

Onshore wind farm Offshore wind farm Photovoltaic power station

Wind

100,000

–

2328

41.60

0

0

0

Wind

400,000

–

6554

77.84

0

0

0

Solar

20,000

–

4400

29.19

0

0

0

Fuel cell plant

Gas

10,000

9500

7477

396.21

0.00013

0.013

130

IGCC with CCS

Coal

520,000

10,700

6942

154.48

0.015

0.0075

20.6

Polymer electrolyte membrane fuel cell Electrolyzer Hydrogen tank

Hydrogen

–

–

2500

62.5

–

–

–

Hydrogen Hydrogen

– –

– –

100 30

2 0.15

– –

– –

– –

Table F.2 Environmental cost of the pollutant emission [20–22]. Pollutant

Environmental cost ($">$/ton/yr)

Considered environmental damaged effect

SO2 NOX CO2

1494.71 296.66 34.80

Premature mortalities, illness, reduced timber and crops yields, reduced visibility effect, and depreciation of man-made materials Agriculture, energy production, water availability, human health, coastal communities, and biodiversity

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